Meta-reinforcement learning provides another interesting and novel perspective on exploration. As noted earlier, meta-reinforcement learning gives rise to activation dynamics that support learning, even when weight changes are suspended. Importantly, the learning that occurs in that setting involves exploration, which can be quite efficient because it is structured to fit with the kinds of problems the system was trained on. Indeed, exploration in meta-reinforcement learning systems can look more like hypothesis-driven experimentation than random exploration [1]}, [2]}. These properties of meta-reinforcement learning systems make them an attractive potential tool for investigating the neural basis of strategic exploration in animals.
